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Evolution of Quantum Computing: A Systematic Survey on the Use of Quantum Computing Tools

Authors:

Abstract

Quantum Computing (QC) refers to an emerging paradigm that inherits and builds with the concepts and phenomena of Quantum Mechanic (QM) with the significant potential to unlock a remarkable opportunity to solve complex and computationally intractable problems that scientists could not tackle previously. In recent years, tremendous efforts and progress in QC mark a significant milestone in solving real-world problems much more efficiently than classical computing technology. While considerable progress is being made to move quantum computing in recent years, significant research efforts need to be devoted to move this domain from an idea to a working paradigm. In this paper, we conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing and analyze them from an application and Quantum Computing perspective. We present quantum Computing Layers, Characteristics of Quantum Computer platforms, Circuit Simulator, Open-source Tools Cirq, TensorFlow Quantum, ProjectQ that allow implementing quantum programs in Python using a powerful and intuitive syntax. Following that, we discuss the current essence, identify open challenges and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
††*Corresponding author: hshahria@kennesaw.edu
Evolution of Quantum Computing: A Systematic
Survey on the Use of Quantum Computing Tools
Paramita Basak Upama
, Md Jobair Hossain Faruk
, Mohammad Nazim
, Mohammad Masum
§
, Hossain Shahriar
§
Gias Uddin
, Shabir Barzanjeh
‡‡
, Sheikh Iqbal Ahamed
, Akond Rahman
Department)of)Computer)Science,)Marquette)University,)USA)
Department of Software Engineering and Game Development, Kennesaw State University, USA
Department Computer Science; Kennesaw State University, USA
§Department of Information Technology, Kennesaw State University, USA
Schulich School of Engineering, University of Calgary, Canada
‡‡Department of Physics and Astronomy, University of Calgary, Canada
Department of Computer Science, Tennessee Tech university, USA
{
paramitabasak.upama
}@marquette.edu,){
mhossa21,)mnazim
}@students.kennesaw.edu,){
§hshahria,)mmasum
}@kennesaw.edu,)
{
gias.uddin,)‡‡shabir.barzanjeh
}@ucalgary.ca,)){
*sheikh.ahamed
}@marquette.edu,))))&)))){
arahman
}@tntech.edu)
AbstractQuantum Computing (QC) refers to an emerging
paradigm that inherits and builds with the concepts and
phenomena of Quantum Mechanic (QM) with the significant
potential to unlock a remarkable opportunity to solve complex and
computationally intractable problems that scientists could not
tackle previously. In recent years, tremendous efforts and
progress in QC mark a significant milestone in solving real-world
problems much more efficiently than classical computing
technology. While considerable progress is being made to move
quantum computing in recent years, significant research efforts
need to be devoted to move this domain from an idea to a working
paradigm. In this paper, we conduct a systematic survey and
categorize papers, tools, frameworks, platforms that facilitate
quantum computing and analyze them from an application and
Quantum Computing perspective. We present quantum
Computing Layers, Characteristics of Quantum Computer
platforms, Circuit Simulator, Open-source Tools- Cirq,
TensorFlow Quantum, ProjectQ etc. that allow implementing
quantum programs in Python using a powerful and intuitive
syntax. Following that, we discuss the current essence, identify
open challenges, and provide future research direction. We
conclude that scores of frameworks, tools and platforms are
emerged in the past few years, improvement of currently available
facilities would exploit the research activities in the quantum
research community.
KeywordsQuantum Computing, Qubits, Quantum Sensing,
Platforms and Tools for Quantum Computing, Evolution of
Quantum Computing
I. INTRODUCTION
Quantum computing relies on properties of quantum
mechanics to compute problems that are beyond the reach of the
existing classical computers and achieved significant
advancements in the past few years [1], [2]. Intersecting of
various techniques including physics, mathematics, computer
science, and information theory paved to initiate a perfect
domain, quantum computing capable of performing calculations
deemed unachievable for classical computers. Compared to
classical computers, quantum computers have high
computational power, less energy consumption, and exponential
speed [3]. It is attainable by controlling the behavior of tiny
physical objects or microscopic particles including atoms,
electrons, and photons that transfer digital information.
In quantum computing, zero or one bit (one-bit) of
information is encoded using two orthogonal states of a
microscopic object known as quantum bit or qubit [4]. Having
both 0 and 1 simultaneously as its value is called
“superposition”. Also, they have a property known as
“entanglement” and is based on the changing of the state of one
Qubit also changes the state of another, even residing at a
distance. Qubits acquire both digital and analog nature that
brings the quantum computers into tremendous computational
power [5]. Several quantum algorithms have already been
developed, Grover’s algorithm for searching and Shor’s
algorithm for factoring large numbers in popular [6].
TABLE 1: DIFFERENCES BETWEEN QUANTUM COMPUTING AND
CLASSICAL COMPUTING
Quantum Computing
Classical Computing
Calculates with Qubits, that can
have values 0 o1 or both
simultaneously
Calculates with transistors,
that can have values either 0
or 1
Power increases exponentially in
proportion to the number of Qubits
Power increases linearly with
the number of transistors
Have high error rates
Have lower error rates
Operates at close to absolute zero
temperature
Operates at room temperature
Much secured to work with
Less secured to work with
Suited for big/complex tasks, such
as-optimization problems, data
analysis and simulations
Suited for everyday
processing tasks
Quantum programming languages are essential to translate
complex ideas into instructions to be executed by a quantum
computer. They facilitate the discovery and development of new
quantum algorithms, as well as executing the existing ones [7].
There is a number of key differences between Quantum
Computing and Classical Computing and provided in Table 1.
Quantum algorithms are already being applied in a variety of
industries including healthcare, finance, manufacturing,
cybersecurity, and blockchain. Optimization problems for
scheduling and route planning, search algorithms, sampling and
pattern matching, quantum encryption are a few of them. In
healthcare, accelerating drug discovery, drug design, optimizing
therapy/treatment, probable time to market new drugs are
possible due to Quantum Computing in healthcare industries.
Drafting trading strategies and detecting market instability for
financial services seems to be plausible because of it too.
Besides, advertisements strategy and product marketing,
software verification, and validation are much easier with
emerging Quantum Computing.
The main motivations for this study are the supreme nature
of quantum computing, its advantages in solving real-world
problems much more efficiently than classical computing and
identifying open challenges of this emerging research field. The
contribution of this paper is two-folds:
We conduct a systematic review and present the progress of
quantum computing.
We identify related frameworks, tools, and platforms that
facilitate quantum computing.
We discuss the recommendation and future work in the area
of our study.
The rest of the paper is organized as follows: In Section II,
we discuss related work for Quantum Computing evolution and
Tools followed by research methodology in Section III. Section
IV provides details of various platforms and tools for Quantum
Computing followed by explaining circuit simulators of
quantum computing in Section V. Section VI explains tools and
software while Section VII discusses the current situation of the
field and its challenges followed by providing recommendations
for future research in Section VIII. Finally, Section IX concludes
the paper.
II. RELATED WORK
Quantum computing is trying to optimize algorithms in
various fields of computers by implementing and harnessing the
power of qubits in a quantum environment or computer.
Quantum evolutionary algorithm (QEA) and the divide-and-
conquer idea of cooperative coevolution evolutionary algorithm
(CCEA) are used to overcome the low solution efficiency,
insufficient diversity in the later search stage, slow convergence
speed and a higher search stagnation possibility of differential
evolution (DE).
Six algorithms in solving six test functions from CEC’08
under the dimensions of 100, 500 and 1000 resulted in an
improved differential evolution (HMCFQDE) with higher
convergence accuracy and stronger stability [8]. Quantum
evolutionary concepts were executed by quantum superposition
and entanglement which showed a logarithmic growth rate of
the number of evaluations of fitness functions needed to identify
a sufficiently accurate solution, and the depth of its quantum
circuits is O (1) with a significant impact on the effect of
quantum noise on computation.
Google’s classical PageRank algorithm has also explored its
quantum implementations with the growing content being
uploaded online [9]. The Quantum PageRank algorithm was
simulated on a six node web network for observation against the
PageRank algorithm. Quantum PageRanks were able to have
faster stabilization and consistent PageRank ordering by adding
a little noise during the computation of the Kossakowski-
Lindblad master equation.
M. Bidlo and P. Zufan [10] performed a comparative study
on the evolutionary design of quantum operators. Genetic
Algorithm and Evolution Strategy are applied, each in four
different setups, and evaluated on three case studies: the 2-qubit
Controlled-NOT gate, 3-qubit entanglement operator and 4-
qubit detector of an element with the maximum amplitude. The
newly applied QR decomposition achieved 100% success in 3-
qubit entanglement using both GA and ES and the best statistical
evaluation in case of the 4-qubit operator. Quantum computing
has superior computational strengths than the classical computer
and NP-hard problems have been tackled with this method.
Graph partitioning (NP-Hard) graph problem has been
solved by U. Chukwu et al. [11] utilizing two quantum-ready
methods of QUBO (quadratic unconstrained binary
optimization) and constrained-optimization sampler. Both
approaches often delivered better partition than the purpose-
built classical graph partitioners.
Stuart M. Harwood et al. [12] focus on variational quantum
eigensolver (VQE) which is a hybrid quantum-classical
algorithm. The authors adopted variational adiabatic quantum
computing (VAQC) to propose an improved VQE method that
continuously parameterized Hamiltonian via the quantum
circuit. The proposed technique VAQC has the ability to
successfully find good initial circuit parameters to initialize
VQE. The method was evaluated with two examples from
quantum chemistry combined with other techniques that provide
more accurate solutions than conventional VQE, for the same
amount of effort.
S. Boyapati, S. R. Swarna and A. Kumar [13] evaluated the
performance of a quantum computed prediction mode using
Quantum Neural Networks (QNN). This computational
mechanism using quantum computing and the neural network
will track the live operations and form the dynamic route
changes in the real-time scenario. This real-time scenario
worked with a 95% accuracy rate with its accuracy differing
based on the number of connecting nodes being considered.
Rosa M. Gil Iranzo et al. [14] addresses the limitations of
quantum computing interfaces that facilitate learning the
emerging paradigm. The authors proposed a layer to create
proper learning environments for performing calculations
without facilitating the understanding of the principle of
quantum computing concepts. The proposed work focuses on
Human-centered computing that shall facilitate various levels
including high school, university, and the research level. This
research is novel around the domain of design of quantum
computing interfaces integrating science and technology.
III. RESEARCH METHODOLOGY
The systematic literature review [15], [16] has been
conducted to find the current innovations that are either
completely new or modification of existing approaches for the
study on the Evolution of Quantum Computing, depicted in
Figure 1. A “Search Process” was implemented to acquire
research papers that address our topic of study. Thus, specific
search strings were applied during our analysis in scientific
databases which contained the keywords, “Quantum
Computing”, “Quantum Computing Evolution” and “Quantum
Computing Tools”.
Figure 1: Systematic Literature Mapping Process [17]
The scientific databases that were used for procuring these
papers including: (i) IEEE Xplore, (ii) ScienceDirect, (iii) ACM,
(iv) Springer Link, and (v) ResearchGate. We adopt a screening
process to find the most relevant papers by studying the paper
title followed by reading and understanding the abstract and
conclusion from screened papers.
TABLE II: GENERALIZED TABLE FOR SEARCH CRITERIA
Scientific Database
Initial Keyword Search
Total Inclusion
IEEE Xplore
109
4
ScienceDirect
50
2
ACM
50
2
Springer Link
25
0
Research Gate
17
0
Total
251
8
An exclusion and inclusion process based on (i) duplicate
papers (ii) full-text availability, and (iii) papers that are not
related to Quantum Computing was conducted to prune off
research papers that had aspects that were not related to our
literature review as well as duplicates that appeared during the
initial search. Table III. displays the details of the inclusion and
exclusion process.
TABLE IIII: OVERVIEW OF EXCLUSION AND INCLUSION
Category
Condition (Inclusion)
Title
Quantum Computing
Evolution, Tools, Methods
Duplicate
Papers
Papers are not duplicated in
different scientific databases
Relativity
Proposed approaches reflect
Quantum Computing
Text
Availability
Papers that are available in
the full format & in English
Firstly, the filtration procedure had a time constraint that
allowed research papers published from the years 2016 to 2022.
Furthermore, additional filters were placed in each database to
narrow our search of relevant research materials. IEEE Xplore
included Conferences and Journals while ScienceDirect
required us to select Computer Science as the subject area and
research articles for article type. Springer Link and Research
Gate did not provide any unique or relevant topics of research.
We also included ACM scientific database where we included 2
papers for this study. Total 251 research papers were found
during the initial search but an in-depth screening process that
accounted for the publication title, abstract, experimental results
and conclusions shortened the list to 8 papers for our study.
IV. PLATFORM USED FOR QUANTUM COMPUTING
Quantum Platform (QP) or Quantum Computer Platform
(QCP) is a family of lightweight, open-source software
frameworks for building responsive and modular real-time
embedded applications in Quantum Computing [18]. Quantum
Computer Platform consists of two layers: Quantum Computing
Layer and Classical Computing Layer [19], [20] depicts in
Figure 2. In this section, we present our findings on platform
used for quantum computing with a research question- what are
the platforms used for quantum computing in the literature?
Figure 2: Quantum Computer Platform Architecture
A. The Quantum Computing Layers
An optimal set of hyperparameters allows performance
improvement as well as avoid performance issues like
overfitting. The Quantum hardware covers Qubits which are
surrounded by superconducting loops for the physical
realization of Qubits. It also consists of the internally connected
circuitry for Qubit control operations. Quantum Processing Unit
includes Quantum registers, logic gates, and memory. The
Quantum-Classical Interface houses the required hardware and
software in order to provide interfacing between the classical
computers and a Quantum Processing Unit (QPU). Lastly, the
Classical Computing Layer includes the final components-
Quantum Programming environment, Cloud data Centre and
Business Applications.
B. Characteristics of Quantum Computer Platform
Low-level Programming: The Quantum Computers
currently in use are built on low-level programming.
They are based on quantum logical gates and handle
computational steps to execute in QPU.
Heterogeneous: In QCP the technical specifications are
heterogeneous in nature for both software and hardware.
Some examples of QCP (or QP) are IBM, Microsoft, D-
wave, Google.
Remote software development and deployment: All the
QCP vendors provide Quantum Computing software
development frameworks for leveraging quantum
processors that can only be accessed remotely from the
cloud. Only a small part of the programming tool stack
is deployed on the local machines. So, the programmers
access quantum software remotely for development and
testing.
Quantum algorithms: The popular algorithms help in
gaining speed and communicating with other computing
tasks that are running on QCP. Additionally,
programmers need to either identify or design suitable
algorithms to solve the problems in hand.
Portability of Software: The software developed by the
QCP owners are currently native in nature. This software
always follow its own standards, proprietary
programming API and predefined tools. Examples of
software will be found in the next segments of this paper.
V. CIRCUIT SIMULATOR FOR QUANTUM COMPUTING
The working procedure of a quantum circuit is shown with
the following diagrams of a simulator called “Quirk” [21], one
of the most used simulators for Quantum Computing. In the
circuit of Figure 3, there are two |0|0 qubits. When the gates
are dragged onto these circuits the output changes accordingly
[22].
The Hadamard gate or H-gate [23] is a quantum logic gate.
It redistributes the probability of all the input lines. As a result,
the output lines have an equal chance of being 0 and 1. Dragging
an H-gate onto one of the |0|0 circuits from Figure 3 will give
the circuit in Figure 4. Implementing the H-gate in this circuit
the output has a 50% chance of being measured ON, or 1.
Figure 3: Quantum Circuit with Quirk
Adding a new gate to the circuit of Figure 3 will give the
circuit in Fig. 4. The new gate is called the Pauli X gate, classical
the quantum equivalent of the NOT gate. This gate flips the input
state, so a 0 as input becomes 1 as output, and vice versa. From
the circuit of Figure 5 that is visible that the chance of measuring
a 1 is 100%. Some common quantum logic gates with their
associated matrices are shown in Table IV below.
Figure 4: Use of Hadamard Gate on the circuit
Figure 5: Use of Pauli's X-gate on the circuit
TABLE IV: COMMON QUANTUM LOGIC GATES WITH THEIR ASSOCIATED
MATRICES
Matrix
"
0 1
1 0
"
"0 −𝑖
𝑖 0"
"1 0
0 −1"
1
2
"
1 1
1 −1
"
"1 0
0 𝑖"
"1 0
0 𝑒!"/$ "
*
1 0 0 0
0 1 0 0
0 0 0 1
0 0 1 0
*
*1 0 0 0
0 1 0 0
0 0 1 0
0 0 0 −1*
*1 0 0 0
0 0 1 0
0 1 0 0
0 0 0 1*
*
*
*
1 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0
0 0 1 0 0 0 0 0
0 0 0 1 0 0 0 0
0 0 0 0 1 0 0 0
0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 1
0 0 0 0 0 0 1 0
*
*
*
VI. TOOLS AND SOFTWARE FOR QUANTUM COMPUTING
Some of the tools and software used for Quantum
Computing are discussed in this section with a research
question, what are the tools and software are being used for
quantum computing in the literature?
A. Cirq
Cirq [24], [25] is an open-source Python library for writing,
manipulating and optimizing Noisy Intermediate Scale
Quantum (NISQ) circuits, and also for running them against
quantum computers and simulators illustrate in Figure
6. Moreover, it can be used with OpenFermion-Cirq which is a
platform for developing quantum algorithms for chemistry
problems. Cirq is not an official Google product, but Google AI
Quantum Team is promoting it.
B. TensorFlow Quantum
TensorFlow Quantum (TFQ) [26] is a quantum machine
learning library that is being used for prototyping of hybrid
quantum-classical machine learning models by Google illustrate
in Figure 7. It works with Cirq [27] to provide quantum
computing primitives compatible with existing TensorFlow
APIs, along with high-performance quantum circuit simulators.
Figure 6: A general overview of Cirq
Figure 7: How TensorFlow Quantum works with Cirq
C. ProjectQ
ProjectQ [28] is an open-source software framework that
allows users to implement quantum programs in Python using a
powerful and intuitive syntax (Fig 8). After that it can translate
these programs to any type of back-end, either a simulator
running on any classical computer or an actual quantum chip
including the IBM Quantum Experience platform.
D. CirqProjectQ
CirqProjectQ [29] is a port between ProjectQ and Cirq that
provides two main functions. As the first function, it has a
Circuit Simulators &
Quantum Cloud Service
Cirq
(programming framework)
Research Libraries and Tools
(OpenFermion, TensorFlow Quantum,
ReCirq, Pennylane etc.)
Cirq
Local simulator
(20-30 Qubits)
Quantum
Hardware
(not
currently
available
to general
users)
Quantum Engine (access
by invitation only)
ProjectQ backend that converts a ProjectQ algorithm to a cirq
circuit. Secondly, it can decompose ProjectQ common gates to
native Xmon gates to simulate a Google quantum computer with
ProjectQ.
Figure 8: Working procedure of ProjectQ
E. Microsoft Quantum Development Kit
Microsoft Quantum Development Kit [30], [31] appears to
supercede their earlier LIQUi|>software. This kit features a new
quantum programming language Q#. It works with integrating
the Visual Studio development environment (Figure 9 and
Figure 10).
Figure 9: Working procedure of Microsoft Quantum Development
Kit
F. IBM Quantum Experience
IBM’s 5 qubit gate-level quantum processor on the web
allows the users to apply to get access to it. The IBM Quantum
Experience [32], [33] website shows four modules, a short
tutorial with instructions to use it, a quantum composer to
configure quantum gates for the qubits, a simulator to simulate
the configuration before running it on the actual machine, and
finally access to the machine itself to run the configuration and
view the results (Figure 11). It has an associated software API
called QISKIT.
Figure 10: IDE of Microsoft Quantum Development Kit (integrated
with Microsoft Visual Studio)
Figure 11: How IBM Quantum Experience works
G. Rigetti Forest and Cloud Computing Services (QCS)
The Rigetti Forest suite [34], [35] consists of a quantum
instruction language Quil, an open source Python library pyQuil,
a library of quantum programs called Grove and a simulation
environment called QVM (Quantum Virtual
Machine). QCS provides a virtual classical computing
environment alongside the Rigetti quantum hardware. It comes
pre-configured with Rigetti’s Forest SDK and provides the users
with a single access point to the QVM and QPU backends.
H. CAS-Alibaba Quantum Computing Laboratory
The CAS-Alibaba Quantum Computing Laboratory [36] has
built several superconducting quantum computers. Their
hardware systems are available through an online interface for
Quantum
Development
Kit
Quantum
Computer
Topological
Qubit
the users to write quantum circuits, execute them, and download
the results over the cloud using a GUI.
I. Quantum Computing Playground
The Quantum Computing Playground [37] is a Chrome
Experiment or web app (Figure 12) uses WebGL to simulate up
to 22 qubits on a GPU. Inside it the users get a basic IDE to
write, compile and run the code; along with some example
algorithms (Grover’s, Shor’s). Also, a debugger and 3D
quantum state visualization tool are there, so users can see
what’s going on inside the little quantum computer. QScript is
the programming language used here, and it is similar to Bash-
like scripting languages.
Figure 12: How Quantum Computing Playground works (simulating
the example of Shor's algorithm)
J. Strawberry Fields
Strawberry Fields [38] is an open-source quantum
programming architecture for quantum machine learning depicts
in Figure 13.
Figure 13: Display How Strawberry Fields works
It uses Python language and consists of a full-stack library
for design, simulation, optimization and quantum machine
learning of several paradigmatic algorithms, such as-
teleportation, (Gaussian) boson sampling, instantaneous
quantum polynomial, Hamiltonian simulation and variational
quantum circuit optimization.
K. Wolfram Quantum Framework
The Wolfram Quantum Framework has more than 5000
built-in options to work on the quantum functions and objects
using Wolfram language [39]. The framework provides
Wolfram notebook to its users with full integration to
Mathematica and Wolfram language, where Mathematica is a
software system with built-in libraries for machine learning,
statistics etc. to develop and simulate several algorithms.
VII. CURRENT SITUATION OF THE FIELD AND ITS CHALLENGES
Today’s Quantum Computers take up an entire room, but
their capabilities are all really small-scale till now. They possess
less than 100 Qubits each which does not seem enough for the
tasks they are up to. Currently, the Quantum Computer with the
highest number of Qubits is China's Zuchongzhi with 66 Qubits
[40], [41]. It is able to perform a sampling task in 1.2 hours that
would take eight years for a Classical Computer to complete. At
present, about 46 countries are engaged in national or
international Quantum research works and developments. Most
of these actions are found to happen in academia and industry
[42], [43].
Lack of good software leads to technological challenges in
Quantum Computation including limited qubit connectivity, too
low gate fidelities, and the requirement of large amounts of
qubits for error correction. Lack of collaboration and exchange
between industry and academia is also a major issue in the
advancement of Quantum Computing. Furthermore, such
computers operate at temperatures close to zero, and
maintaining such a low temperature is always a big challenge.
Today, computers with 70 Qubits fall short of the requirement
of one million Qubits to make economically feasible and viable
Quantum Computers.
VIII. RECOMMENDATIONS FOR FUTURE RESEARCH
Cloud-oriented Quantum Computing has the potential to
overtake future business initiatives and technologies including
cryptography, machine learning (ML), and artificial intelligence
(AI) [44][47]. It seems plausible because of the almost
unlimited memory spaces available in clouds. Besides, shared
hardware could be proved helpful in solving complex tasks with
Quantum algorithms but by using a Classical Computer as its
base.
Looking at the possibilities, Quantum AI tools may provide
the world with autonomous weapons and mobile platforms [48]
[50]. For example, drones made with Quantum AI tools can
achieve heightened sensing, navigation, and positioning options
in GPS-denied areas, as well as altering the course of operation
to avoid enemy countermeasures.
In addition, a new internet possibility with Quantum devices
emerged called the Quantum internet, which is separate from the
internet and links Quantum devices together using
entanglement. It significantly increases the connectivity,
security, and speed of the internet and shows the potential of the
super-secure communication infrastructure that protects
Quantum internet connected devices from cyberattacks to serve
in the field of cryptography. For instance, some scientists in the
Netherlands entangled three one-qubit devices in this manner,
and they successfully communicated and stored information in
a theoretically unhackable manner.
Moreover, Quantum cryptography, ML and AI tools can be
combined to improve intelligence service and its analysis [51],
[52]. Such intelligence services are supposed to be able to break
2048-bit RSA encryption in 8 hours or even less- a task that
would require the world’s fastest supercomputers around 300
trillion years to complete with brute-force methods. Quantum
computers might demand almost 20-million Qubits to perform
it. Advances in this field show possibility of such machines in
25 years. There is a chance of adversarial use of such computing
which can risk national and international security if advances in
Quantum decryption outrun advances in Quantum encryption.
With the advancement of Quantum Computing, ML and AI
problems could be solved in a practical amount of time- reduced
from hundreds of thousands of years to a few seconds.
Although there are many challenges in quantum computing
and accessibility in quantum devices, we study quantum
machine learning for software supply chain attacks [53]. In a
separate study, we also investigate quantum cybersecurity where
we discuss the threats, risks, and opportunities [54].
IX. CONCLUSION
Quantum Computing harnesses the phenomena of quantum
mechanics to solve complex problems that today's most
powerful supercomputers cannot solve. In this paper, we
reviewed the evolution of Quantum Computing where
tremendous efforts and progress mark a significant milestone in
solving real-world problems in recent years. We also present the
progress of various frameworks, tools, software, and platforms
that facilitate quantum computing. Finally, we discussed the
current essence, challenges, and provide a research scope for
future research. The findings of our study indicates that scores
of frameworks, tools and platforms are emerged in the past few
years, improvement of currently available facilities would
exploit the research activities in the quantum research
community.
ACKNOWLEDGEMENT
The work is partially supported by the U.S. National
Science Foundation Awards #2100115, #1723586 and
#1723578, National Institute of Health STTR
Grant#R41GM146313 and SunTrust Fellowship Award. Any
opinions, findings, and conclusions or recommendations
expressed in this material are those of the authors and do not
necessarily reflect the views of the National Science
Foundation, National Institute of Health and SunTrust
Fellowship Award.
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Chapter
The Quantum industry is currently at an embryonic stage. If it is to grow, it will require new markets, and, a workforce with the requisite skills and knowledge to support it. Anticipating this potential growth, this paper will explore capacity building within engineering education on the subject of Quantum Computing (QC). This work has two aims. On the one hand, it seeks to illustrate the need for developing education on the subject, inferred by trends in open literature. On the other hand, it seeks to suggest a starting point for quantum computing education in higher education. Since 2018, a sharp incline can be observed in the number of publications on topics related to QC. These publications are arising within several fields related to engineering including, but not limited to, material science, chemistry and computer science. In response to this trend, this paper will evaluate several third party educational approaches to teaching emergent technologies with a view to developing a model for teaching QC. Due to a lack of precedent in a wide range of industry applications and the current limitations in the state-of-the-art of this technology, the educational model proposed will be one that exploits imagination, as opposed to knowledge acquisition, in the pursuit of new knowledge building.
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Though research on quantum computing continues at a good pace, learning quantum computing requires a deep and clear level of knowledge of a range of domains that diverge from classical computer science. There are not quantum computing interfaces that facilitate learning this new paradigm, only those to illustrate some quantum computations with some accompanying documentation. Moreover, in this documentation, science is mixed with technology, and it is usually even worse because there is no clear distinction between the underlying mathematics and physics concepts. We consider that this must be addressed in the first place in order to create proper learning environments that go beyond performing calculations without facilitating the understanding of the core of quantum computing concepts and their implications. This is a new research line, dealing with the design of quantum computing interfaces integrating science and technology.